Missing data is a pervasive problem in medical and epidemiological research. Multiple imputation (MI), a simulation-based method, is one reasonable approach for handling missing data. Recently, mainstream statistical packages such as SAS, STATA and R have incorporated MI procedures allowing easy access to its use. While in theory MI yields valid results when data are missing at random (MAR), in practice the story is more nuanced. Much of the burden remains on the user for appropriate application in order for validity to hold. For example, MI is not completely straightforward in the presence of categorical variables, derived variables or interaction terms. Further, different software packages not only rely on different methods for imputation but also make different options available for han